3 Executive Summary Growth in e-commerce occurs at a tremendous rate. Over the past decade the continuing development in electronic and mobile capabilities has led to increasingly more opportunities for e-commerce sales. European e-commerce retail sales in 2011 were at 96 billion Euros, of which 9 billion in the Netherlands (Thuiswinkel.org, 2012), and are expected to grow by 12.2% per year, topping 172 billion Euros in 2016 (Gill, 2012). Within the operation of web shops, maximizing turnover can be seen as a key characteristic of success. As such increased research efforts have focussed on factors determining the buying behaviour of online customers. Trust has been identified as the main driver of purchase intentions online (Harris & Goode, 2010), which is in turn influenced by several factors. These factors are summarized under the term e-servicescape: the online environment factors that exist during service delivery (Harris & Goode, 2010). What made the implementation and design of these factors challenging, is the notion that web shop visitors are not homogeneous. Not only do different visitors have different intentions for visiting based on utilitarian versus hedonic purposes, visitors are also heterogeneous in the sense that they are in one of several stages part of the decision making process underlying online consumer purchase behaviour (Butler & Peppard, 1998; Teltzrow & Berendt, 2003; Van der Heijden, Verhagen, & Creemers, 2003). As such, different pages and sections of a web shop are oriented towards supporting one or multiple of these stages and goals. A major deficit of the e-servicescape model as presented by Harris and Goode (2010) is not taking into account these different phases. Furthermore, this theoretical view opposed to the practical nature of web shop operation in which specific pages are optimized in order to maximize revenue and purchases. The main goal of web shop operators is thus to optimise cart value and conversion, defined as the amount of purchasing visitors as opposed to the total amount of visitors having expressed an interest towards a product (Teltzrow & Berendt, 2003). In order to further validate the e-servicescape model and in order to include the different stages of the consumer decision making process, the following research question was developed: Which e-servicescape factors and design rules can be used during different stages of the consumer decision making process to optimise web shop conversion? Based on a literature review encompassing 87 quality academic sources, 37 design rules were extracted. These design rules were part of the e-servicescape factor aesthetic appeal, layout and functionality or financial security and subsequently coupled to one or more of the consumer decision making stages part of this research: search for information, evaluation of alternatives and choice / purchase. Next, 13 semi-structured interviews were held with employees at a web shop operator focussed on web shops of large retailers and brands in order to validate the model. This resulted in the updating of 7 design rules and the adding of 10 additional design rules. Given that the interviews were held at a single company, generalizability of the model is somewhat limited and requires future research. The final model is depicted at the end of the main text of this thesis. Next to the validated e-servicescape model, additional knowledge on two design rules part of the model was created by implementing these rules at a lingerie retailer s web shop using two field experiments. The first experiment focussed on the inclusion of cross-selling functionality on the cart page and the use of unique selling points (USPs) on the cart page. The results showed a negative I

4 impact of cross-selling functionality on cart-to-purchase conversion rates, but not on profitability. Furthermore the USPs did not prove to have an effect on cart-to-purchase conversion, but this might have been a result of the sale that was active during the experiment. The second experiment focussed on the effects of using a single-page versus a multi-page checkout on checkout-conversion. The dataset was however too limited to draw scientific conclusion. The limited amount of data available was not only a limitation for the checkout experiment, but also for the cart page experiment. In made it difficult in both cases to correctly identify the effect of time based covariates. Additionally the research setup and field nature of the experiments did not allow for the measuring of concepts such as trust and purchase intentions, as were some technical restrictions present. The main scientific contributions of the research are threefold. First of all the e-servicescape model is expanded to include consumer decision making process stages and specific and detailed design rules. Second the field validation of the model by means of interviews allowed for the inclusion of field knowledge, best practices and sentiment. Finally future research opportunities regarding specific design rules and topics were identified. The main managerial implications of the research are also threefold: the validated e-servicescape model allows for the day-to-day use of a practical model with design rules in order to influence and optimise visitors purchase intentions. Furthermore cross-selling was identified as having a negative effect on cart-to-purchase conversion but no impact on revenues in the specific case of the lingerie retailer under discussion. It implies the careful consideration of where and when to use cross-selling to increase cart values due to the potential negative effects on conversion. The final implication is formed by the experiment execution: it is important to carefully consider experiment setups, data collection methods and data analysis methods as well as the way specific implications are established. A solid academic approach and well thought-out plan are needed to derive significant and meaningful results. II

5 Preface After six years of education, I can finally say that I do not for a second have doubts about my choice for the education field of Industrial Engineering and specifically Innovation Management at Eindhoven University of Technology. Balancing hard engineering skills and knowledge and more soft marketing and psychological skills encompassed what I was looking for and have found. The combination of different fields of interests has both provided me with challenges and opportunities; challenges in prioritising and in time and stress management on the one hand and opportunities for expanding knowledge and both personal and professional enrichment on the other hand. It feels both strange and relieving that this report marks the end of my six year education at Eindhoven University of Technology. However it is with confidence that I now enter the career market being the next step lying in front of me. I would like to express my gratitude to those that have helped me in the realization of this thesis report as the ending of my Master s Education. First of all to my first TU/e supervisor Joost Wouters, for kindling my enthusiasm for marketing during his courses and for his useful advice and aid in providing structure and simplicity where there first was complexity. I also would like to express my gratitude to my second supervisor Martijn Willemsen for supporting me and keeping me on the correct path of providing academic value in this thesis. My thesis research would not have been possible without the support of and opportunities made available by Docdata Commerce. I owe gratitude to my company supervisor Jurriaan Hes for providing me with insights and his support during the realisation of my experiments and to all Docdata Commerce employees for their support and willingness to cooperate with both my thesis project in general and specifically the interviews I held with them. The past six years of education and the completion of my thesis would not have been as successful or at least triple as difficult without the continuous support of my parents Rob and Ineke, not-so-little brother Wouter and my loving girlfriend Meike. I thank them for their criticism, support and faith. To all those that I have not seen the chance of thanking here, please know that I am grateful for your support, enthusiasm and spare time company. Were it during my study projects, my part time jobs, my countless hours of practicing Irish dancing or during any other wonderful activity, I have learned that practice makes perfect as longs as you keep an open, creative mind. Maarten van Haperen December 13, 2012 Scribendi recte sapere est principium et fons. - Horatius, De Arte Poëtica 309 III

9 1. Introduction Growth in e-commerce occurs at a tremendous rate. Over the past decade the continuing development in electronic and mobile capabilities has led to increasingly more opportunities for e-commerce sales. European e-commerce retail sales in 2011 were at 96 billion Euros, of which 9 billion in the Netherlands (Thuiswinkel.org, 2012), and are expected to grow by 12.2% per year, topping 172 billion Euros in 2016 (Gill, 2012). Not only the rise of fully internet-based companies is a major development, e-commerce has also become increasingly important for retailers that operate brick-andmortar shops. This is due to the fact that the growth in e-commerce sales also implies the diminishing of offline spending. A web shop is hence not merely an opportunity but has become a necessity. Within the operation of web shops, maximizing turnover can be seen as a key characteristic of success. As such increased research efforts have focussed on factors determining the buying behaviour of online customers. Trust has been identified as the main driver of purchase intentions online (Harris & Goode, 2010), which is in turn influenced by several factors. These factors are summarized under the term e-servicescape: the online environment factors that exist during service delivery (Harris & Goode, 2010). This term stemmed from research on designing the servicescape in physical environments, with a focus on all factors during a service delivery in for example a shop or hospital (Bitner, 1992). What made the implementation and design of these factors challenging, is the notion that web shop visitors are not homogeneous. Not only do different visitors have different intentions for visiting based on utilitarian versus hedonic purposes, visitors are also heterogeneous in the sense that they are in one of several stages part of the decision making process underlying online consumer purchase behaviour (Butler & Peppard, 1998; Teltzrow & Berendt, 2003; Van der Heijden, Verhagen, & Creemers, 2003). As such, different pages and sections of a web shop are oriented towards supporting one or multiple of these stages and goals. A major deficit of the e-servicescape model as presented by Harris and Goode (2010) is not taking into account these different phases. This opposes the practical nature of web shop operation in which specific pages and sections are optimized in order to maximize revenue and purchases. The main goal of web shop operators is thus to optimise cart value and conversion, defined as the amount of purchasing visitors as opposed to the total amount of visitors having expressed an interest towards a product (Teltzrow & Berendt, 2003) Research question and contribution In order to further validate the e-servicescape model and in order to include the different stages of the consumer decision making process, the following research question was developed: Which e-servicescape factors and design rules can be used during different stages of the consumer decision making process to optimise web shop conversion? By providing an answer to the research question, the contributions of this research are both theoretical and practical. The theoretical contributions are twofold. First they consist of validating the e- servicescape model as proposed by Harris and Goode (2010) by incorporating knowledge from different academic publication regarding e-servicescape factors and knowledge available in the field. Second the e-servicescape model is extended to include the different stages of the consumer decision making process and different purposes of web shop pages and sections, a criticism on the original model that only looked at a web shop in general. 1

10 1.2. Research approach In order to answer the research question a multi-step approach was considered, as depicted in Figure 1.1. The first step in the research approach consisted of validating and detailing e-servicescape design rules on the basis of academic publications and where applicable assigning them to specific stages in the consumer decision making process. This resulted in a theoretical e-servicescape design rule model adapted towards the consumer decision making process. The second step in the research consisted of validating the theoretical framework with knowledge available in the field of web shop operations. This resulted in a validated and extended e-servicescape design rule model adapted towards the consumer decision making process. Two of the design rules were investigated further in order to generate more knowledge on their implementation. Academic publications Literature review E-servicescape design rule model Design rule investigation Field experiment validate Knowledge from field Case study Figure 1.1 Research approach 1.3. Research method The research approach outlined was established on the basis of three research methodologies: a systematic literature review, a single embedded case study and a field experiment. The systematic literature review was based on 35 quality academic sources. It established the e-servicescape model, the consumer decision making process and a theoretical framework incorporating the design rules based on the combination of the two. Next a series of interviews was conducted on the basis of a single embedded case study at a web shop operator, in order to validate and extend the framework. Two design rules were selected from the framework for further research, which was done using an explanatory field experiment at the web shop of a large lingerie retailer Thesis outline Based on the research question and corresponding research approach, the remainder of this thesis is outlined as depicted in Table 1.1: chapter two depicts the literature review and single embedded case study on which the e-servicescape design rule model adapted towards the consumer decision making process was based and validated. Chapter three focuses on establishing a set of hypotheses based on two design rules selected for further investigation using field experiments. The experiment design and method are described in chapter four after which the results from the experiment are presented and discussed in chapter five. Chapter six subsequently focusses on providing conclusions drawn on the basis of the entire research, before discussing its limitations and identifying opportunities for future research as well as theoretical and managerial implications. 2

13 2. Validating and extending the e-servicescape model 2.1. Online trust Due to the low search costs and effort involved in online shopping, visits without purchase intentions will be more common online as opposed to offline in the future (Moe & Fader, 2004). Over the past decade increasing research was performed with regards to stimulating the intention to purchase by means of different drivers. One of the main drivers for purchase intentions as posed by several researchers in the early 2000 s is trust (Van der Heijden et al., 2003). Other studies have shown this to be true: trusting beliefs regarding the web site had a significant positive effect on intention to buy from it (Stewart, 2003: 5) and In particular, the emotion of trustworthiness has been emphasized as one of the most important factors for the successful completion of commercial transactions (Jinwoo Kim & Moon, 1998: 2). Additionally, research has shown that poorly functional designed web shops result in large amount of lost customers (Silverman, Bachann, & Al-Akharas, 2001; J. Song, Jones, & Gudigantala, 2007) Defining trust In order to be able to identify the roles of trust on purchase intentions, a definition was needed: Online trust can be defined as an Internet user s psychological state of risk acceptance, based upon the positive expectations of the intentions or behaviours of an online merchant (Y. D. Wang & Emurian, 2005: 42). This view was supported by Lee & Turban, (2001: 79): the willingness of a consumer to be vulnerable to the actions of an internet merchant in an Internet shopping transaction, based on the expectation that the Internet merchant will behave in certain agreeable ways, irrespective of the ability of the consumer to monitor or control the Internet merchant. The definitions show three aspects part of the trust definition: it involves two parties (trustor and trustee) that need each other for mutual benefits, it involves risk and it involves the trustor believing that the trustee will behave according to the risk involving behaviour (Siau & Shen, 2003). Shopping online brings risks that do not exist in traditional shopping, such as the absence of a physical quality check before purchase and difficulties in safeguarding financial and privacy information once handed over to the merchant (Lee & Turban, 2001). Both M. Lee and Turban (2001) and Siau and Shen (2003) constituted three factors as main elements of online trustworthiness that build on the previous definitions: ability, benevolence and integrity. Ability deals with the merchant s skills and competencies in performing online. Benevolence focuses on whether the consumer is convinced that the merchant wants to do things right over merely maximizing profit. The final factor integrity relates to the perception of the consumer that the trusted party is honest and acts in correspondence with acceptable principles. Online trust is a combination of these three factors and is furthermore context and situational specific. In case of e-commerce a challenge lay in the fact that trust issues not solely involve the consumer and the web shop merchant. There are also trust issues between the consumer and the computer system through which that consumer makes transactions (Lee & Turban, 2001). In this research focus was put on the type of trust mentioned first, as it was expected that interaction between consumers and computer systems became more mainstream. This makes the web shop merchant trust issues more relevant. Important to consider in this perspective is also the propensity to trust of individual consumers; the way cues related to trustworthiness are magnified or reduced based on the personality of the consumer (Lee & Turban, 2001). 5

14 Influencing trust There are several aspects that influence a consumer s trust in a web shop. It has to be taken into account that trust is not merely the outcome of technical requirements but involves communication as well (Chadwick, 2001). Hence trust can be influenced both implicit and explicit on both emotional and cognitive levels. It can take place before visiting a web shop, during the visit and after an online transaction (Egger, 2001). Three important aspects for influencing trust are past experience, external factors and the e-servicescape. The first aspect involves past experiences, influencing a consumers attitude towards and trust in web shops in the future (Salam, Iyer, Palvia, & Singh, 2005). Bad experiences may negatively affect trust, whilst good experiences may make it easier for a consumer to trust. The experiences do not only constitute experiences related to the same web shop in the past, but also include experiences at other web shops. A study by Wang and Emurian (2005) also showed that respondents with bad previous experiences with web shops, such as being cheated on, gave comparatively lower ratings when asked to value overall trustworthiness of a new web shop. The second aspect involves external factors. Trust can be influenced by factors inside the scope of control of the web shop that moderate the impact of both good and bad previous experiences as described earlier. Such factors include a well-established offline reputation, the consumer s perceived size of the company, where bigger is better, and communications such as press releases, advertisements and promotions, (L. H. Kim, Qu, & Kim, 2009; Lee & Turban, 2001; Salam et al., 2005; Van der Heijden et al., 2003). The same goes for external factors outside the direct scope of control of the web shop, such as news reports, evaluations and product recalls, but also guarantees, legal rules and procedures. The latter factors influence what is called institution based trust, which in turn will also affect purchase intentions of consumers (Salam et al., 2005). It can even lead to a situation where a consumer does not trust a web shop, but makes a purchase nonetheless as there is trust in the control systems and (legal) procedures in place (Van der Heijden et al., 2003). The third and final important aspect is based on the e-servicescape model, see Figure 2.1 (Harris & Goode, 2010). The e-servicescape was defined as the online environment factors that exist during service delivery (Harris & Goode, 2010, p. 231). Hence by identifying and changing trustinfluencing factors on a web shop, both trust and correspondingly purchase intentions can be Figure 2.1 E-Servicescape, factors and sub-factors (Harris & Goode, 2010) 6

15 influenced. The importance of web shop design was also stated by Wang and Emurian (2005, p. 43): In other words, one key consideration in fostering online trust is to build a trust-inducing e- commerce interface. Examining the web shop by looking whether it is from a well-known brand, reading product information and looking for symbols of security approval are important riskreduction strategies by consumers (L. H. Kim et al., 2009) and are an integral part of the web shop design. By designing the e-servicescape of a web shop in such a manner that trust and subsequently purchase intentions are maximized, will have a direct impact on conversion. If users have higher purchase intentions than can be converted into purchases, a web shop owner can increase revenues on the basis of its current visitor set, without the direct necessity to attract additional visitors to gain more visitors with a high enough purchase intention E-servicescape Once visiting a web shop consumers immediately form quick impression of the web shop, which they seek to confirm with additional information and impressions they receive from the web shop. This goes as far as interpreting information to suit the initial impression: on the same web shop, consumers with a negative first impression interpret information negatively, whilst consumers with a positive initial impression interpret the same information in a positive way (Stewart, 2003). The initial impression can be influenced by the design of the e-servicescape. Before going into detail on the e- servicescape, the origin and background of the concept are reviewed Concept Bitner (1992, p. 58) has conceptualised the term servicescape as being the built environment (i.e., the manmade, physical surroundings as opposed to the natural or social environment) and has posed that it has a major impact on both consumers and employees in service organisations. Important aspects involve ambient conditions, space and function, and signs and symbols. Although factors for brickand-mortar stores and web shops converge, there are some major differences as the online aspect creates unique challenges at different transaction steps. The e-servicescape (also termed cyberscape or virtual servicescape) was thus described as the online environment factors that exist during service delivery (Harris & Goode, 2010: p. 231). This definition was widely supported by other researchers (Jeon & Jeong, 2009; Vilnai-yavetz & Rafaeli, 2006; Williams & Dargel, 2004). Several main factors form the e-servicescape of web shops. Aspects such as brand name cannot be directly influenced and are therefore not part of the e-servicescape, although indicated by Fang & Salvendy (2003) as a major factor impacting a shopper s trust in web shop. The influential aspect that is part of the e-servicescape is the placement and the number of displays of the brand name. The same goes for the mention that people prefer to shop online over shopping at brick-and-mortar stores because it is more convenient. Although this is most likely the case, the aspect by itself does not improve purchase intentions. However, the way it is made clear in the e-servicescape to consumers why online shopping should be preferred and is more convenient is, as well as providing a picture of a physical building to induce trust in the online shop (Stewart, 2003). As a starting ground for an e-servicescape literature review, the model by Harris & Goode (2010) was used. According to their model, the e-servicescape consists of three factors as depicted in Figure 2.1. The first factor is aesthetic appeal, the second factor is layout and functionality and the third factor is financial security. With regards to aesthetic appeal the focus lies on online ambient conditions that create an attractive and alluring servicescape from the customer perspective. Layout and functionality focuses on a contrasting aspect. Layout is focussed on the arrangement, 7

16 organisation, structure and adaptability of web shops. Functionality focuses on the extent to which these aspects facilitate the conversion and service goals. The final factor, financial security deals with the way in which consumers executing their purchase intention perceive the payment process as secure and feel safe to complete it E-servicescape factor Aesthetic appeal The first of the three factors part of the e-servicescape is aesthetic appeal. Y. D. Wang and Emurian (2005: 51) stated that design is more than an artistic interface. This was supported by other research, stating that aesthetics deals with the sensory experience elicited by an artefact, and the extent to which this experience tallies with individual goals and attitudes (Vilnai-yavetz & Rafaeli, 2006: 248). The definition was supported by Cai and Xu (2011: 161) who stated that aesthetics is a holistic perception of design principles and individual objects (on a web shop), ( ) closely connected to attention and understanding, ( ) that significantly affect human affect and emotion. Good visual design not only provides visual pleasure, but also comfortable reading and ease of use which (Y. D. Wang & Emurian, 2005). Three sub-factors are part of aesthetic appeal, as seen in Figure 2.1: originality of design, visual appeal and entertainment value (Harris & Goode, 2010). Aesthetic appeal plays an important role in today s consumers online consumption style, which shifted from utilitarian purposes to a combination of utilitarian and hedonic purposes in which recreation and entertainment have become more important aspects (Van der Heijden et al., 2003; J. Y. Wang, Minor, & Wei, 2011). This shift and the different goals of users need to be taken into account during the different purchase related stages in a web shop, moreover as the initial brief exposure of a consumer to a web shop page immediately results in an aesthetic impression. This impression correlates with the consumer attitude to that page and the entire web shop (Cai & Xu, 2011) E-servicescape factor Layout & functionality The second of the three factors part of the e-servicescape is layout and functionality. It encompasses which design aspects are included on a web shop and the placement of these aspects. Four sub-factors are part of layout and functionality, as seen in Figure 2.1: usability, relevance of information, customisation and interactivity. The overall goal is to create a web shop with easy-to-use navigation, frequent updating, minimal download times, relevance to users and high quality content (Palmer, 2002: 153). The importance of layout and functionality was underlined further by Cai and Xu (2011: 162): When a web site is intuitively understandable in its design, it facilitates users interaction with the web site and gives them a strong sense of control, knowledge of where to focus their attention and deep cognitive enjoyment. As a result users may experience a state of flow whereby they have a distorted sense of the passage of time and achieve an intrinsically enjoyable experience E-servicescape factor Financial security The third and final factor part of the e-servicescape is financial security. It encompasses security as experienced while making (or planning to make) an electronic or internet payment. Financial security is an important factor of the e-servicescape (Siau & Shen, 2003), moreover as in 2001 over 80% of online shoppers abandoned their shopping carts before completing a transaction (Hausman & Siekpe, 2009). Although partly explainable by the fact that many web shop visitors use the shopping cart as a wish list and comparison tool between web shops, reducing this percentage by increasing financial security, results in a direct increase in revenue. 8

17 Although the penetration of internet payment is increasing, there will still be customers that have not made (many) internet payments. Hence it is important to consider factors that determine the potential adoption and usage of payment methods by customers. He and Mykytyn (2007) found that customer s willingness to adopt and use depends mostly on the overall design quality of the web shop, on perceived risks and perceived benefits and on the payment features offered. Liang and Lai (2002) state the importance of functional support using a good design to meet the customer s needs online. This is supported by Ranganathan & Ganapathy (2002) who state that the overall quality of the web shop design is important for the performance of the web shop. Next to the overall design quality, two factors part of the e-servicescape aspect financial security that can influence customer s willingness to adopt and use online payment methods: perceived security based on perceived risk and ease of payment, as seen in Figure Consumer decision making process A major deficit of the e-servicescape by Harris and Goode (2010) is that the model focusses on web shop at an abstract level. It does not take into consideration that consumers proceed through various phases when making a purchase decision and that different pages and sections of the web shop facilitate one or several of these phases. Before focussing on the consumer decision making process it has to be noted that consumers can be placed on a continuum of two extreme values based on behavioural characteristics (Carmel, Crawford, & Chen, 1992; Tomes, 2000): goal-directed behaviour, focussed on making a purchase, and experiential behaviour, focussed on browsing. The goal-directed and experiential behaviour are respectively characterised by extrinsic versus intrinsic motivation, utilitarian benefits versus hedonic benefits and directed versus non-directed search (Hong, Thong, & Tam, 2004). The phases a consumers proceeds through when looking to buy a product which requires limited to extensive problem solving behaviour, were depicted in Figure 2.2 (Butler & Peppard, 1998; Miles, Howes, & Davies, 2000). Although the process has been set up as being linear, iterations and feedback loops are very important as it is unlikely the consumer will follow a strictly linear approach in his decision behaviour. The first phase constitutes a consumer realising or being attended to the fact that a new product or service is required. Next follows the search for information in which different alternatives are derived, followed by a phase in which these alternatives are evaluated and a phase in which a choice on the product and purchase location are made. The final stage focuses on satisfaction and loyalty behaviour resulting from a purchase, leading either to future purchases or disappointed customers. As the thesis focused on conversion optimisation, focus was placed on the information search phase, evaluation phase and purchase phase. Post-purchase behaviour was taken into thought however, as converting existing customers to repeat buyers has proven to be over six times cheaper than converting new customers (Silverman et al., 2001). Problem recognition Information search Evaluation of alternatives Choice / purchase Post-purchase behaviour Figure 2.2 Online consumer purchase and decision behaviour (Butler & Peppard, 1998) 9

18 The approach towards viewing the consumer purchase and decision process as an iterative, but generally linear process view, was coupled to the intention based view identified by Song and Zahedi (2005), stating that a consumer visits a web shop with the intention to make a current purchase decision, revisit the web shop in the future or to repurchase in the future. Consumers in the information search phase will likely have the intention to revisit the web shop in the future, not necessarily purchasing in their current visit, whilst consumers in the choice and purchase phase are likely to have a direct purchase intention. The phase focussing on the evaluation of alternatives could indicate both web shop visit intentions. It may be the case that customers are looking to immediately buy after evaluating alternatives or that they may postpone the purchase action to a later visit, indicating that there could both be a return visit or current purchase intention. The final intention by Song and Zahedi (2005), the intention to repurchase, was coupled to the post-purchase behaviour stage after the actual purchase. As customers evaluate their purchase on a product and on a web shop level in this phase (Butler & Peppard, 1998), this is the moment that determines whether or not a buyer is likely to visit the web shop again in the future with the intention to purchase again. Focussing on the current purchase intention in the model by Song and Zahedi (2005), the consumer buying and decision making process was linked to the business oriented customer life cycle perspective as seen in Figure 2.4 (Teltzrow & Berendt, 2003). From a business point of view, a company tries to get suspects (targeted customers) to visit the web shop, making them prospects. Once on the web shop, the prospects are converted to customers, which will have different loyalty behaviours based on purchase satisfaction. The suspects to prospects process can be coupled to the problem recognition and information search phases by Butler and Peppard (1998), whilst the prospects to customer process can be coupled to the evaluation and choice phase. It was interesting to investigate the conversion process of consumers more closely, as these are consumers who have already expressed an interest in buying and only need to complete their purchase. The purchase process consists of four steps, as seen in Figure 2.3: seeing a product impression, performing a product click-through, effecting a basket placement and making a product purchase (Teltzrow & Berendt, 2003). A product impression is for example a product image on the category overview page. Clicking on this product in order to get the product page is the following step. If a consumer decides to buy, the product will be placed in the shopping basket. This stage poses challenges as research by Close and Kukar-Kinney (2010) indicated that consumers also use a shopping cart to compare products between web shops. The final stage is completing the purchase by providing credentials and a shopping address and by paying for the product. At this time price negotiation options such as vouchers or coupons, (multiple) shipping options and payment options become important in the decision making by the consumer (Silverman et al., 2001). A similar view was oriented on sequential Nominal User Tasks (NUTs). NUTs are in this case tasks a customer has to perform in order to place an order on a web shop (Sismeiro & Bucklin, 2004). Three tasks were identified. First a customer has to complete the product configuration, for example selecting the product colour, desired size and quantity, and then place the product in the basket. Next the purchase stage as identified by Teltzrow and Berendt (2003) is split in two tasks. The customer first has to provide a complete set of personal information. This can be done by either providing all details or by logging in if an account was made in the past. The final task is confirming the order by providing payment data or making a payment. 10

19 Prospects M1 Saw impression M2 Clicked-through M3 Placed in basket Customer made purchase nm1 = nc nm2 = nc nm3 = nc nm4 = nc Figure 2.3 Online consumer purchase and decision behaviour (Butler & Peppard, 1998) 2.4. Integrating the e-servicescape and the consumer decision making process Taking both the e-servicescape factors and sub-factors and the consumer decision making process, a list of design rules to maximise purchase intentions was established on the basis of an academic literature review including 35 quality academic sources, as depicted in the final model in Table 2.1 at the end of this chapter and in the detailed model depicted in Appendix A. The design rules were coupled to the consumer decision making process phases information search, evaluation of alternatives and choice / purchase as these phases are most prominent on a web shop. The design rules were selected to be applicable to one or multiple of these phases on the basis of either the article they were extracted from (i.e. provide a link back to shopping occurs in the cart and as such automatically only in the choice / purchase phase) or on the basis of common sense (i.e. display out-of-stock products and sizes, which can only be applicable during the information search phase) Validating and extending the e-servicescape model A single embedded case study at a large web shop operator was used to validate and the model established on the pervious pages. The company was founded in 2000, originally focussing on providing system management hardware and services until 2005 an order fulfilment company took a minority stake. In 2010 that company extended its share and gained sole proprietorship in the e- commerce operations organisation that is currently employing 28 FTEs, as seen in Appendix B. The organisation operates web shops for large brands and retailers and its portfolio includes a lingerie retailer, a luxury leather products brand and a company selling printing supplies. Choice / purchase Suspects Prospects Customers Repeat customers Rep.customers elsewhere Not reached ns Not acquired np No sale nc One-time customers Figure 2.4 Online consumer purchase and decision behaviour (Butler & Peppard, 1998) 11

20 Case study method The method by which the best practices were derived was a single embedded exploratory case study. A single case study was used as only employees at the e-commerce fulfilment company were interviewed to extract knowledge, as the case study was exploratory in nature as it was aimed at identifying design rules and hypothesis regarding factors that influence conversion in web shops (Yin, 2009). The unit of analyses was best practices concerning web shop conversion optimisation present within the e-commerce company. The best practices were based on four specific cases of web shops that made over 100,000 Euros revenue per year or processed over a million visitors per year, next to comments that were made regarding other web shops. Using web shops with more traffic reduced the risk of missing or overstating on optimisation techniques that are too small to measure and do not or unintended got significant outcomes. In selecting cases a choice was made to only investigate projects of web shops that have were recently launched on the basis of a well-known, strong and established offline brand, or web shops that have existed for over a year, in order to be able to analyse the servicescape factors in general. It was expected that there would be substantial moderating influences for brands and web shops that had yet to establish themselves. The case study data was acquired using the data collection principles of Yin (2009) in order to ensure a basic level of reliability and validity. Reliability deals with the repeatability of a study where equal results should follow in case of a study reproduction under the same conditions. Validity deals with concepts, measurements and conclusions being well-founded and consists of construct validity, whether a measurement tools measures what is intended for, internal validity, dealing with the causality of findings and external validity, generalizability beyond the current study. The first principle included maintaining a chain of evidence by being clear about subsequent steps in the case study and having readers understand the structure of the research in order to make clear on what grounds conclusions were based. The second principle was creating a case study database. It ensured reliability by allowing other investigators to review the evidence used in the case study report, being notes, documents, interviews and other materials, although access to this database was limited and confidential. The third and final principle was using multiple data sources, also known as triangulation, and ensured construct validity. The data was collected by interviews that were held with staff that was directly involved with operating aspects of web shops and could have impact or knowledge on how to design the e- servicescape. The interviewees consisted of three customer support employees, four shop managers, two marketers, the managing director, one intern and an interview with a representative of the company that provided the payment interface, one of the largest payment service providers globally. Two customer support employees were included to test the interview format and because they received direct consumer feedback in their daily operations. Four shop managers were included due to their involvement in web shop operations, with two shop managers focussing on shop management of web shops for retailers and brands and two shop managers focussing on web shops that were fully oriented on SAPOS, Sales At Point Of Service. Two marketing engineers were included as they support the shop managers, the managing director was included because of his knowledge on the e- commerce business environment and one academic intern was included due to his focus on predicting online purchase intentions. The software engineers were not interviewed as they stated they did not have any knowledge on web shop optimisation and were fully focussed on technology, being able to build nearly anything required by other departments. The representative of the payment company was included to shed light specifically on the payments section in the e-servicescape model. 12

21 The interviews were semi-structured, see 0, based on the e-servicescape (sub-)factors mentioned earlier (aesthetic appeal, layout & functionality and financial security) in order to validate and confirm knowledge from the literature based model, whilst at the same time leaving room to include other factors not included in the model (Van Aken, Berends, & Van der Bij, 2007). As the interviews were semi-structured based on open questions, there was ample possibilities to venture into specifics regarding literature based and field based factors. This prevented over-focussing and ensured a clear, broad view on the matters discussed. The results of the audio-taped interviews were reviewed by the interviewees themselves to ensure construct validity (Yin, 2009) Case study results The results of the case study were analysed as follows: every design rule was declared either supported or commented on per interviewee in case the interviewee had knowledge regarding the design rule and it was discussed in the interview. After generalising these results over all interviewees, a decision was made whether the design rule was supported or needed revision. As there were no design rules that were contradicted by a large portion of the interviewees, no drop decision were made. A support or comment decision was made on the basis of two characteristics: support ratio and individual expertise. The first characteristic is the ratio of the amount of individual supports compared to the total amount of supports and comments. The general rule was established that at design rule should have supports or comments in least six of the eleven interviews and that there should be a 67% majority of support statements to come to a support decision. In case of a lower ratio, the design rule was revised to include the comments that arose during the interviews. The second decision characteristic is the expected knowledge of the individual interviewees, compensating that a designer is expected to be better informed about design rules regarding aesthetics than for example the representative of the payment service provider. This made it possible to support or revise a design rule going against the outcome of the first decision characteristic. Next to supporting or revising design rules, it was also possible to add new design rules. Again the decision characteristics above were taken into account, considering that at least three interviewees should independently mention a new decision rule in order for it to be included. In case of adding design rules, the corresponding decision making process stages are based on common logic. The overall model with revised and added was discussed with the marketing department and found appropriate. The anonymised, individual and aggregate level interview results have been included in 0. Consecutively the e-servicescape model was updated and validated, as can be seen in 0, resulting in the overall model as depicted on the following pages in Table

22 Table 2.1 Validated e-servicescape model Decision making process stage Choice / purchase Evaluation of alternatives Information search Design rule Include original designs and signs such as logos. Animate logos for increased effectiveness and impact, but sparingly to avoid distraction. Whilst adhering to standard and common design rules, make sure the design fits the web shop and brand proposition. If possible, implement references to an offline brand and retailer. Design should be colourful by a good selection, placement and combination of colours. Design should be diverse, by visual richness, dynamics, novelty and creativity. Design should be simple by showing unity, homogeneity, clarity, orderliness and balance. Design should show craftsmanship by modernity and integrating simplicity, diversity and colourfulness. Provide large size high quality product images supported by schematic product characteristics. Provide logos, certificates and other visual cues early on to enhance feelings of trust. Originality of design Visual appeal Do not distract users with aesthetic designs during checkout. The careful use of people on pictures can provide context and transfer emotion and feeling. Create a design that flows fluently from home page to checkout with focus on support for decision and transaction processes Use a consistent tone of voice that suits the target audience. Be scarce with vivid entertainment as it decreases shopping cart use. Create entertainment by providing thoughtful use of colour and typography based on functionality. Create entertainment by social aspects, interactive elements and inspirational design. Provide entertainment by regularly updating the web shop so consumers get the feeling it evolves. Aesthetic appeal Entertainment value 14

23 Table 2.1 Validated e-servicescape model (continued) Decision making process stage Choice / purchase Evaluation of alternatives Information search Usability Relevance of information Customisation Interactivity Design rule Build multiple ways of navigation based on ease-of-use by different types of consumers and the actions it facilitates that continuously shows the breadth and depth of the web shop. Consider that the size and location of text and graphics determine users attention based on F-shaped scanning patterns. Create a clean and uncluttered design, without unnecessary text and graphics and minimum loading times and system crashes, that behaves as user expect. Provide clear organisation and layout without distractions. Provide a link back to shopping. Create a consistent and logical user flow from home page to checkout. Provide contact information, preferably including a (free) number, to reach the consumer support department. State competitive advantages regarding the quality of product offerings and services clearly throughout the web shop. State information regarding price, features, inventory information and order related charges as early on as possible. Provide information that is accurate, consistent and specific, supported by full size pictures. Provide information that is accurate, consistent and specific. Display out-of-stock sizes, but remove permanent out-of-stock products and colours. Provide information from a consumer point of view whilst keeping them in a continuous flow. The location, type and implementation of cross-selling, especially in case of limited data and business rule, should be considered due to conflicting results. Specify customisation towards decision and transaction processes. Add features supporting direct interactivity between visitors and sales or support employees. Add interactive functionality that is potentially useful or influences site usage and navigation. Change text and colours when hovering over actionable text and images. Layout & functionality 15

24 Table 2.1 Decision making process stage Choice / purchase Evaluation of alternatives Information search Validated e-servicescape model (continued) Perceived security Ease of use Design rule Display trusted and independent seals and certificates of approval throughout the web shop. Ask only strictly necessary information and exclude marketing questions. Explicitly state what information is stored and not stored. Display trusted and independent seals and certificates of approval. Create a consistent and logical user flow from home page to checkout. Allow for checkout completion without registration or using an account. Provide actionable feedback and error messages and only if strictly necessary. Provide information regarding the different checkout steps as well as the current location. Provide the option of credit card payments, regular payment types and payment types that function as the extension of existing methods. Take the products sold and different target audiences into account when designing single or multi-page checkouts both for speed and confirmation. Financial security 16

25 3. Experiment design based on the e-servicescape model On the basis of the validated e-servicescape model, two design rules were selected for further investigation by means of field experiments. The selection was based both on academic value to the field and on practical opportunities available to execute experiments in order to generate knowledge. Because of the nature of the field experiments, it was not possible to measure purchase intentions and other psychological concepts at a visitor level. Instead conversion, the amount of purchasing visitors as opposed to the total amount of visitors having expressed an interest towards a product (Teltzrow & Berendt, 2003), was used to identify the effects of experiment variations. This was based on the logic that by increasing trust, consumers will have higher purchase intentions, subsequently resulting in overall higher web shop purchase rates. The first design rule selected focussed on cross-sell functionality: The location, type and implementation of cross-selling, especially in case of limited data and business rule, should be considered due to conflicting results. Considering that the topic of recommendation engines is a highly active research field, a decision was made to focus on generating on the effects of cross-selling at one particular page of the web shop: the shopping cart. The cart page was selected as cross-selling is an important variable for the cart page influencing the balance of getting visitors to enter checkout and complete their order on the one hand and stimulating higher cart values on the other end. Hypotheses regarding the design rule are established in paragraph 3.1. The second design rule selected focussed on the type of checkout used: Take the products sold and different target audiences into account when designing single or multi-page checkouts both for speed and confirmation. Little academic research has been done on the topic of single-page and multi-page checkouts. As such a field experiment is used in order to create a first indication towards the effect size and direction of different checkout variations on checkout conversion rates. The hypothesis regarding the design rule is established in paragraph Improving cart page conversion When comparing to a clean and minimised cart page design, the main benefit of adding cross-selling would be to increase revenue and as such web shop profitability. Several interviewees however also pointed out critical remarks. These remarks focussed on situations with little or insufficient data and resources to successfully implement cross-selling on the cart page, in which cases cross-selling could have a negative impact on cart to purchase conversion rate due to the offering of non-matching products, subsequently leading to declining web shop revenue. As such a hypothesis was stated to investigate the effect of cross-selling in the cart compared to a transaction oriented cart design on web shop revenue and conversion rate. It was hypothesised that the extended revenue from cross-selling counterweighs the decrease in conversion rate: Hypothesis 1 A A clean cart page design oriented on completing a transaction performs equal to a cart page design oriented on enhancing cart value when compared on cart-to-purchase conversion rate and cart value. Next to cross-selling the effect of an additional design rule was investigated. Several design rules focused on providing important information regarding ordering as early on in processes as possible so that customers are informed beforehand and are not to be brought in doubt regarding the order conditions late in the process. As such it was thought to be beneficial to again explicitly state unique 17

26 selling points (USPs) of the web shop in the cart, with both the goals to inform and persuade potential buyers. Given the nature of the situation described, a hypothesis was stated to compare the cart page design enhanced with USPs with a clean transaction oriented cart page design. It was hypothesised that the USPs would have a positive effect on the cart-to-purchase conversion compared to the transaction oriented cart page design: Hypothesis 1 B A transaction oriented cart page design supported by USPs has a higher cartto-purchase conversion rate than a transaction oriented cart page design without USPs Improving checkout conversion Four factors were addressed in the ease of use design rule focussed on the type of checkout to be used: speed, confirmation, product and target audience. In the setting in which this experiment was able to run, product and target audience were already set. The products were lingerie articles in the low-tomedium price range and the target audience consisted of females between the ages of sixteen and fifty. The need for the remaining two factors, speed and confirmation, were investigated using two checkout designs: a single-page and a multi-page checkout. It is expected that the target audience in this specific case has sufficient knowledge with purchasing and paying online and with the internet in general. Furthermore it is expected that once they have selected the products of their choice and are ready to proceed to checkout, they want to pay swiftly with less confirmation rather than in a more time and click consuming manner with more confirmation. The latter is supported by Bucklin and Sismeiro (2003) stating that operators should consider pages with more information on each page to reduce the number of page views needed to complete a transaction. As such it was hypothesised that a single-page checkout outperforms a multi-page checkout when it comes to checkout-to-purchase conversion rates: Hypothesis 2 A single-page checkout has a higher checkout-to-purchase conversion rate than a multi-page checkout. 18

27 4. Experiment method The hypotheses established in the previous chapter were tested in two separate on-site field experiments. This chapter details the method used for collecting data in paragraph 4.1 and the method of data analysis in paragraph 4.2. The quality safeguarding of the experiment data is also discussed, in paragraph Data collection The on-site field experiments were run at a large lingerie retailer s web shop. The lingerie shop formula, operated in the Netherlands by an e-commerce fulfilment partner, focuses on being personal, service oriented and stocking high quality lingerie products with a decent price/quality balance. The web shop was launched after three months of developing in February At the time of the experiment nearly 500 products with over 4000 SKU s (different sizes and colours) of three brands were sold online, whilst the web shop processed over unique visitors each month. The web shop pages and page variations that were part of the field experiment were identified and (re)designed according to the design rules under investigation. After approval by the retailer and the fulfilment partner following design iterations, the experiments were executed. In the case of the cart page and checkout experiments, A/B software tools were used to respectively equally assign visitors to different cart page variations and to randomly assign visitors to different checkout variations in a one (single-page) to four (multi-page) ratio. An open source web analytics software package was used to record and anonymously store individual click stream data on a page view level, as depicted in Experiment Cart page In order to test the hypotheses that providing USPs on the cart page positively influences conversion and that orienting the cart page towards increasing shopping cart value influences conversion and cart value, two variations were designed. These variations were based on a control condition, which is depicted version next to the other variations in the conceptual design in Figure 4.1 and the actual design in 0. The first variation was the Control variation. It encompassed no signs or functionality of either Figure 4.1 Cart page designs; Left: variation 1 (Clean, control), middle: variation 2 (USPs), right: variation 3 (Cross sell) 19

28 cross-selling or additional USPs and was considered the most functional oriented cart page design. The variation was based on the design rules not to distract users with aesthetics and to provide clear organisation and layout without distractions. The second variation USPs focuses on clearly depicting unique selling points and other important information on the web shop early on in the checkout process. This way consumers should be less distracted and perceive less risk later on in the checkout process, as based on the design rules to state advantages regarding the services of the web shop and providing checkout and order related information as early on in the process as possible. The USPs used were delivery time, return policy, the ability to pay in a secure way and kind service offered by the web shop. Furthermore the logos of several banks were depicted. These specific USPs were used as they are promoted throughout the web shop, hence providing consistency and not providing too much new information to the consumer, as the remainder of the cart page already requires the processing of new information. The third and final variation Cross sell is focussed on providing the opportunity of recommending articles to consumers in order to stimulate cart value, as such investigating the effect of cross-selling in the cart based on relative low amounts of relational data between products available. It showed the label matching articles on top with two matching products below, based on a product-based predefined set of matching products that related on the topic of whether or not a product is from the same designer line. In case of several products, the recommendations were selected randomly (although persistent in the case of a page refresh) from the set of recommendations available. The recommendations were displayed with a product image, brand name, product name and price. In case the product depicted was part of a promotion, all prices including mark-offs were shown. When clicking on one of the products the product popped out and showed again the product image, brand and product name, but this time supported by detailed product information, the article number and the option to choose a colour and size as well as a button to directly add the product to the shopping cart Experiment Checkout In order to test the influence of minimising the steps and actions a consumer must complete to place an order whilst balancing confirmation of information, two checkout designs were tested: a multipage and a single-page checkout. These checkouts included the checkout steps as depicted in Figure 4.2. The first step focussed on acquiring the personal information of the consumer, albeit that it was proceeded in the multi-page checkout by acquiring the consumers adress to check whether an account already existed or not. The second step was focussed on determining the invoice address and the shipping address. The third and final step aimed at completing the purchase by selecting a payment method and subsequently either entering payment information or temporarily leaving the retailer s web shop to do so. After successful submission of the personal and shipment information and completing the payment procedure, a success page with order information was shown. The first design was a multi-page based checkout, focussed on confirming at every step the Cart Step 1 Login / register Step 2 Select adress Step 3 Choose payment Step 1* Enter adress Step 3* Cancel payment Success Order confirmed Figure 4.2 Checkout flow 20

29 information users entered in preceding steps. As depicted in Table 4.1 and shown in 0, the different checkout steps were spread out over several pages. Next to the required information, the right hand side of the page showed an overview of the order to be placed, as well as several unique selling points for the web shop and a clickable DigiCert security seal. Furthermore all information not strictly necessary for completing the checkout was removed from both the header and the footer. The second design was a single-page based checkout, focussed on letting users complete the checkout process as quickly as possible. As depicted in Table 4.1 and shown in 0, the different checkout steps were shown on a single-page below one another. Due to this design no adress was required to be entered by consumers to enter the checkout and as such consumers were only able to register for an account after completion of their order. As with the multi-page checkout, the right hand side of the page showed an overview of the order and additional information, whilst the header and footer were stripped of non-vital information. Table 4.1 Implementation of checkout steps Checkout step Multi-page Single-page Enter adress Page 1 Not included Login / register Page 1 Login: 1 st page section Register: 2 nd page section Select shipment and invoice address Page 2 3 rd page section Choose payment method Page 3 4 th page section Success Success-page Success-page 4.2. Data analysis The experiment design by Montgomery and Runger (Montgomery & Runger, 2007), shown in Figure 4.3, requires the identification of a dependent variable, determined on the basis of a hypotheses, controllable (independent) variables, being the different design variations based on the e-servicescape factors playing a role in conversion optimisation, and finally uncontrollable factors. One of these uncontrollable factors was considered to be the day of the week on a working day (Monday to Thursday) versus weekend level (Friday until Sunday), as it not was not possible to gather enough longitudinal data to conduct a viable analysis on the factor. An additional uncontrollable factor was Controllable factors x 1 x 2 x n Input Visitors Web shop conversion process Output Transactions (y) z 1 z 2 z n Uncontrollable (noise) factors Figure 4.3 Experiment design (Montgomery & Runger, 2007) 21

30 considered to be the time of day, which was split into morning (from 6.00 AM until PM), afternoon (from PM until 6.00 PM) and evening / night (6.00 PM until 6.00 AM), as one would expect that visitors behave differently during morning and evening hours. A final uncontrollable factor considered was cart value. Based on the click stream data several significant variables were calculated focussed both on the general data and the specific individual experiments as explained in the subsequent sub-paragraphs. After collection, corrupt data was removed from the dataset, specifically mobile visitors as after the experiments it showed that a cookie bug was preventing half the mobile users from paying. Next the data was analysed on the basis of Logistic Regression and Analysis of Co-Variance (ANCOVA) techniques, suitable for analysing A/B/n- and multivariate experiments, using the statistical software package SPSS. The choice for the specific method is dependent on whether the dependent variable is dichotomous or continuous. On the basis of the analyses the hypotheses were tested and conclusions were drawn Analysis experiment Cart page As implied in the hypotheses regarding the cart design, the aim of the cart experiment was to optimise conversion, defined as a consumer that visits the cart page and the success page (based on (Butler & Peppard, 1998)). As such all the consumers completing their purchase received value 1, whilst others received value 0. { ( ) ( ) The combination of the dependent variable, the independent variable being the cart variation and the uncontrollable factors, formed up the model as depicted in Table Analysis experiment Checkout As implied in the hypotheses regarding the checkout design, the aim of the checkout experiment was to optimise conversion, defined as a consumer that enters the checkout and reaches the success page (based on (Butler & Peppard, 1998)). As such all the consumers completing their purchase received value 1, whilst others received value 0. A limitation in the measurement possibilities of the singlepage checkout was neglecting the situation in which consumers started entering personal data on the single-page checkout page without completing all information. { ( ) ( ) The combination of the dependent variable, the independent variable being the checkout variation, and the uncontrollable factors, formed up the experiment model as depicted in Table 4.2. Table 4.2 Variable Experiment models Cart page Experiment Checkout Dependent Complete purchase Complete purchase Independent Cart variation Checkout variation Uncontrollable Weekend Time of day Cart value Weekend Time of day Cart value 22

31 4.3. Research quality Using an experiment as a means to identify factors and their magnitude requires taking several quality dimensions into account, being reliability and construct, internal and external validity. Reliability deals with the repeatability of a study. If the study were to be repeated under the same conditions, the same results should follow. Securing reliability was done by using a structured, documented way of working both whilst setting up the experiment and whilst performing the data analysis. Construct validity deals with whether the measurement tool actually measures the concept being studied. This is one of the major issues in the field experiments, as the field setup did not allow for the measuring of characteristics and psychological concepts such as trust and purchase intention at a visitor level. Instead key performance indicators focussing on conversion were identified based on academic literature. Future research should focus on measuring the underlying concepts that likely resulted in the outcome of the current experiment. Next to construct validity, internal validity deals with the causality of results. This was safeguarded and made open to discussion by identifying the conversion process in the literature review and using indicators to measure several steps in this process during the experiments, even though the experiment did not allow for the measuring of characteristics and psychological concepts as discussed before. Additionally, external validity deals with the generalizability of the study, which was controlled for by keeping the uncontrollable factors at a minimum. In order to do so, these factors and their potential effects on the output variables were identified as much as possible and included as covariates. Clearly describing the context of the experiment furthermore makes clear to what level the results are extendable to different contexts. 23

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33 5. Experiment results and discussion On the basis of experiments designed in the previous chapters, data was gathered and analysed. This chapter details the results of the individual independently run analyses, starting with the cart page experiment in paragraph 5.1, followed by the checkout experiment in paragraph 5.2. For all experiments it goes that data generated by the web shop operators both directly and via means of additional software tools was excluded. Furthermore visitors using a mobile device were excluded due to technical issues on mobile platforms influencing the behaviour of visitors. Additionally, analysis assumptions regarding independence of observations were considered tenable, as visitors shown more than one experiment variation were excluded from the dataset Experiment Cart page Data for the cart page experiment was gathered between June 20, 2012 and July 6, In this 17 day time period 58,937 visitors visited the web shop of which 2,736 visitors (4.7%) was displayed one of the cart page variations. This is taking into account the exclusion of visitors that were shown multiple variations and visitors that showed anomalies in their cart and checkout pattern. Additionally, inspection of the data and potential outliers resulted in the identification of one outlier in the cross sell variation where a cart value of over 300 Euros occurred, whereas throughout all variations the next maximum values were all around 200 Euros. Even though the purchase has been validated, the case was excluded from the dataset in the value -base model as there was a clear impact on the results threatening generalizability. The main characteristics of the main variables in the cart experiment dataset can be found in Table 5.1 and in 0 for the covariates day of the week, time of day and cart value. Table 5.1 Descriptive data cart experiment Final step completed Conversion Variation Visitors Cart Login / register Shipment Payment method Cancel Success Cancel & success Mean Std. dev. Total 2, %.484 Clean ,1% 99 10,7% 43 4,7% 63 6,8% 10 1,1% ,7% 8 0,9% 38.6%.487 Unique selling points ,3% 88 9,8% 38 4,2% 46 5,1% 9 1,0% ,9% 6 0,7% 37.6%.485 Cross selling ,2% ,4% 36 3,8% 50 5,3% 6 0,6% ,3% 3 0,3% 35.6%.479 The descriptives depict that a large portion of users do not get past the cart page. The clean page appears to have the highest cart-to-checkout conversion rate, but shows the largest portions of visitors leaving the checkout on the shipment- and payment method-page when compared to the other variations. This is opposed to the cross-selling variation that sees most checkout visitors drop out at the login/register-page. 25

34 Baseline conversion model - Logistic regression The analysis performed is a logistic regression. Three models were analysed. The first model only included the purchase variable as dependent variable and the cart variation as the independent variable. Next cart value was entered as independent value and finally day of the week and time of the day. Before conducting the regression the accompanying assumptions are discussed. Due to the logistic nature of the dataset, we assume a linear relationship between the logit of the outcome variable and the combined independent variables. Additionally we assume that no important variables are omitted and no extraneous variables are included. Based on the dataset and variables available these assumptions are tenable. Two additional assumptions are that the observations are independent and that the independent variables are measured without error, which are both tenable due to the experiment setup. A final assumption is the absence of multicollinearity in the independent variables. Given the VIF values, included in 0, for the time of day dummy variables (morning and afternoon) and a condition index for a dimension that is slightly larger than others, there appears to be multicollinearity between the variables. Given the nature of the variable this was to be expected, however the implication of erratic changes in the coefficient estimates in case of small changes in the model and the data should be taken into mind. The results of the logistic regression can be found in Table 5.2. Starting by comparing the performance of the different models, the constant predicted chance of purchase is with values of 60% to 70% drastically too high in all models when compared to the descriptive conversion rate of 37.2%. Furthermore the adding of the covariates did improve the performance of the model, even though the value of the chi 2 -test did not approximate a significant result. Variable Table 5.2 Results logistic regression experiment cart page B Model 1 Model 2 Model 3 Wald Oddsratio B Wald Oddsratio B Wald Oddsratio Experiment USP variation Cross-selling Cart value Not included * * Day of week: Weekend Not included Not included Time of day Morning Not included Not included Afternoon Constant * * * Model performance Model 1 Model 2 Model 3 Hosmer and Lemeshow chi 2 -test (p = 1.000) (p = 0.567) (p = 0.213) R 2 Nagelkerke Cox & Snell * = p < Interpreting the results from the logistic regression is done by looking at the odds ratio rather than at the coefficient, as it provides an intuitive interpretation: for example a constant odds ratio of 0.3 implies a 30% predicted chance and a variable odds-ratio of 1.1 implies an increase of 10% per 1 increase in the variable, meaning that a variable value of 3 results in a predicted chance of 39%. When looking at the second model (including cart value as a significant covariate), it shows that the predicted chance of completing an order increases with 0.4% per Euro of cart value on the constant, which is relatively large given the average order value of Euros (implying 9.2% base point 26

35 average increase in predicted chance of purchase). Time of day and weekday versus weekend does not significantly influence the predicted chance of completing a purchase. Taking into account all models with all covariates, there were no significant influences of the cart page designs on conversion. The approximate 3% to 4% increase over the 60% to 70% baseline predicted chance of completing a purchase is highly insignificant with p-values around The stronger negative influence of the cross-selling enabled cart page design of approximately 10% over the 60% to 70% baseline predicted chance of completing purchase is insignificant at the p-value of More data is needed to investigate the effects of the cart page designs and more longitudinal data is needed to get more insights into the effects of the covariates Linear conversion model - ANCOVA Given the fact that the cart page design involving cross-selling variation did not significantly differ from the clean cart page design at a small enough α-level, and given that the descriptive analysis of the dataset showed the cart variation likely having an influence on the completion behaviour at different checkout steps, an additional model was created in which all checkout steps were weighted equally. As such a value of 0.00 means that a consumer did not get past the cart page, whilst a value of 0.25 implied the visitor reaching the login / register page, a value of 0.50 implied reaching the select address page, a value of 0.75 implied reaching the payment method selection page and a value of 1.00 implied a consumer completing the purchase. Additionally the payment cancel step was considered to be half way the third step and the success page, as such having a value of An Analysis of Co-Variance was run on the dataset with the covariates weekday versus weekend, time of visit and cart value as covariates and the conversion variable as dependent variable. The covariates were included in the model as they improved the quality of the model during the logistic regression analysis. The ANCOVA implied the testing of four assumptions. Next to the assumption of independence of the observations, which is considered tenable as discussed before, the assumption of a normally distributed population was violated as expected due to the nature of the conversion variable. This does however not necessarily result in issues with the analysis, as ANCOVA is fairly robust to violation of the normality assumption and as such the non-normal distribution only had a small effect on the Type I error rates. Additionally the assumption of homogeneity of variance was tenable, p cart_conv_linear = for Levene s Test of Equality of Error Variances (α = 0.05). The assumption of homogeneity of regression slopes was not tenable for the cart value variable (p < 0.05), but no suitable dummy-coding scheme was identified that both resolved the violation and kept the model easily interpretable. As such it has to be taken into account during the discussion that the cart value covariate may display different effect types at different variable levels. The result of the ANCOVA can be found in 0. Neither the covariate weekday versus weekend nor time of day was significantly related to the linear conversion rate at any level or showed F-values that were close to or larger than the critical F-value (F(1, 2,496) = 5.02). The covariate cart value did prove to be significantly related to the linear conversion rate at F(1, 2,496) = 12.89, p = 0.000, displaying F-values far above critical levels. The effect size proved to be relatively high with B = (t(2,496) = 3.590; p = 0.000), which given the average order value of Euros implies an average increase in conversion of nearly 4%. After controlling for the covariates, the effect of the cart page design on the linear conversion rate was still not significant at the α = 0.05 level, but did show significance near the α = 0.1 level with F(2, 2,496) = 2.090, p = Still the critical F-value (F(2, 2,490) = 3.69) was not reached. The result did however give an indication towards the direction of the cart page design effects. Contrasts 27

36 revealed that the results again point towards small or no differences between the clean cart page design and the cart page design incorporating USPs (p = 0.139) Looking at the differences between the clean cart page design and the cross-selling enabled cart page design, a nearly significant difference was found with p = 0.051, also depicted in Figure 5.1. Cart experiment 'Linear conversion' Cart experiment 'Linear valued conversion' , , , , , , Clean USP Cross-selling 18,00 Clean USP Cross-selling Figure 5.1 Mean values and confidence intervals (α = 0.05) of conversion variables Including cart value ANCOVA As stated in the hypothesis, the focus should not only be on conversion as well as on overall revenue. The experiment influenced cross-selling options available and as such potentially influences cart values. In order to identify those effects, an additional model was created in which the values for the linear conversion model, showing more significant results than the baseline model, were multiplied by the cart value of the visitor. An ANCOVA was performed with the valued conversion as dependent variable, the cart page variation as independent variable and time of the day and weekday versus weekend as covariates. Given the dependent variable, cart value was no longer included as covariate. Running an ANCOVA implied the testing of four assumptions. Next to the assumption of independence of the observations, which is considered tenable as discussed earlier, the assumption of a normally distributed population was violated as expected due to the nature of the conversion variable. This does however not necessarily result in issues with the analysis, as ANCOVA is fairly robust to violation of the normality assumption and as such the non-normal distribution only had a small effect on the Type I error rates. Additionally the assumption of homogeneity of variance was tenable, p cart_conv_linear_value = for Levene s Test of Equality of Error Variances (α = 0.05), as was the assumption of homogeneity of regression slopes, which was tested during the running of the models (p < 0.05). The result of the ANCOVA can be found in 0. Of the covariates, only time of day, specifically morning, was found significantly related to the valued linear conversion at F(1, 2,497) = 4.066, p = 0.044, which is close but not above the critical F-value (F(1, 2,497) = 5.02). The effect size proved to be high with B = (t(2,497) = 2.016, p = 0.044). The other time of day covariates and weekday 28

37 versus weekend variables were not significantly related to linear conversion rate at any day of the week, as were not F-values depicted that were larger or close to the critical F-value. After controlling for the covariates, the effect of the cart page design on the valued linear conversion rate was found not significant with F(2, 2,491) = 0.178, p = 0.488, lower than the critical F-value (F(2, 2,497) = 3.69). Contrasts also revealed only non-significant differences, as seen in Figure Discussion The first hypothesis underlying the cart page experiment was stated as follows: A cart page design oriented on completing a transaction performs equal in revenue based on cart-to-purchase conversion rate and cart value as a cart page design oriented on enhancing cart value. On the basis of the experiment results the first hypothesis was partly rejected (p = 0.051). Looking merely at cart-to-purchase conversion, no statistically significant effect of the cart page design variation was found using a logistic regression. However, when taking into account the different steps part of the checkout using a linear conversion value model, a statistically significant effect was found using an ANCOVA: the clean cart page design outperformed the cart page design providing crossselling functionality. This rejects the hypothesis on the cart-to-purchase conversion rate aspect. It was noted that in both the general and detailed model the cart value proved to be a significant covariate. However, when using a model in which the linear conversion value was multiplied by the cart value, no significant effects of the cart page designs were found and only the time of day covariate morning was found significant. The cart pages designs as such performed equal on the revenue aspect (p = 0.488). From the results one can deducted that it is important to find a balance between the two designs which can be dependent on moderating and environmental business factors. On the one hand conversion is important, but web shop operators should also take into account the revenue made from orders. Higher value orders result in higher profits given product margins and shipping costs, whilst on the other hand a high conversion rates provide the opportunity to achieve economies of scale or to clear out old stock. The second hypothesis underlying the cart page experiment was stated as follows: A transaction oriented cart page design supported by USPs has a higher cart-to-purchase conversion rate than a transaction oriented cart page design without USPs. On the basis of the experiments result the second hypothesis is rejected (p = 0.139). There was no statistically significant effect of providing USPs in the cart page design on cart-to-purchase conversion rates. Three important aspects influenced the results. First of all the presence of only approximately 900 data points per cart variation made measuring clear differences between the two variations difficult, given there was a difference in cart-to-purchase conversion rate of only 1%. More data is needed to clearly measure the effects of providing USPs. The second aspect influencing results was the fact that the experiment was run during a sale period. It was expected that the sales had a larger impact on purchase intentions than the USPs. As such the USPs did no longer have an additional effect on purchase intentions and subsequently conversion rates. This also correlates to the third and final aspect influencing the results, which was the content of the USPs. The USPs were considered relatively weak, being more selling points in general than being 29

38 unique to the web shop. Depicting stronger USPs, which was not approved for this experiment, or moving the USPs to a location on the cart page with more central focus would likely resolve in different results Experiment Checkout The experiment time range was set from August 24, 2012 to September 2, During the ten day period 21,699 visitors visited the web shop, of which 548 visitors (2.5%) was displayed one of the cart page variations (N Checkout Multi-page = 450, N Checkout Single-page = 98). In total 364 orders N Checkout Multipage = 296, N Checkout Single-page = 68) were placed, resulting in a checkout conversion of 66.4 % (M Checkout Multi-page = 0.66, M Checkout Single-page = 0.69). Inspection of the data and did not result in the identification of outliers Results The conversion rates of both checkout variations were close to one another with a difference of only 3% and a small amount of data points. As such first a chi-square test was performed, which equalled p = Therefore it was expected that further analysis would not result in a model with significant variables. However, in order to get an estimate towards the size of the effect and to analyse the role of cart value and time of day, a logistic regression was run. The model included the dependent variable as the purchase variable, the checkout variation as the independent variable, as well as the time of the day and cart value. Due to the short experiment run time, the covariate day of the week was excluded from the model. Before conducting the regression the accompanying assumptions are discussed. Due to the logistic nature of the dataset, we assume a linear relationship between the logit of the outcome variable and the combined independent variables. Additionally we assume that no important variables are omitted and no extraneous variables are included. Based on the dataset and variables available these assumptions are tenable. Two additional assumptions are that the observations are independent and that the independent variables are measured without error, which are both tenable due to the experiment setup. A final assumption is the absence of multicollinearity in the independent variables. Given the relatively high VIF values, included in 0, for the time of day dummy variables (morning, and afternoon) and a condition index for a dimension that is substantially larger than others, there appears to be multicollinearity between the variables. Given the nature of the variable this was to be expected, but not considered a major property given the spurious nature of the dataset. The results of the three logistic regression runs, using no covariates, cart value as a covariate and with time of the day as well as cart value as covariates, can be found in 0. Interpreting the results from the logistic regression is done by looking at the odds ratio rather than at the coefficient, as it provides an intuitive interpretation. Furthermore, given the low value for N, focus was on identifying both significant results and relatively large odds ratios that provide an indication of the effect to be investigated in future research. All three models did not perform extremely well. The experiment variation was not significant in all models. The model including all variables provided significant results that were in line with the cart experiments on the topic of cart value and morning hours being significant. However for all models and interpretations, there is a high risk of over fitting the data given the relatively low amount of data points. This also suits the predicted baseline chance of completing purchase which is higher than 100%. 30

39 Discussion The hypothesis underlying the checkout experiment was stated as follows: A single-page checkout has a higher checkout-to-purchase conversion rate than a multi-page checkout. Given the spurious nature of the dataset, one can only state that more data needs to be gathered including data on covariates, as they appear to have a clear impact on conversion. No significant results can be extracted regarding the single-page versus multi-page discussion other than carefully stating they point, as expected from the descriptive data, towards a positive effect of the single-page checkout design on checkout-to-purchase conversion compared to a multi-page checkout design. It has to be noted that these preliminary unsupported results were specific to this case and the specific checkout designs of this experiment. Despite the inability to draw conclusion from the experiment, it is worthwhile to mention that potential positive results perceived by companies redesigning their multi-page checkout into a singlepage checkout are caused by the mere fact that they are working on building new and optimised checkouts. It could very well be that the improved results would also have been achieved by radically optimising their multi-page checkout. In order to identify the relevance of academic research into checkout design and checkout types, additional practical and laboratory research over multiple web shops is needed. 31

40 32

41 6. Conclusion Effective design of web shops is a key web shop success factor. The e-servicescape model by Harris and Goode (2010) provides a good starting point for building web shops that increase consumer purchase intentions and as such revenues and profitability. A major deficit of the model is however that it does not take into account the different goals and behaviours of individual web shop visitors. Past research has shown consumer to proceed through different phases in a consumer decision making process before actually making an online purchase. As such the following research question was posed: Which e-servicescape factors and design rules can be used during different stages of the consumer decision making process to optimise web shop conversion? Three factors were established on a literature review and a validation oriented single embedded case study: aesthetic appeal, layout and functionality, and perceived security. In total 44 design rules were placed under these factors that were coupled to the applicable consumer decision making process stages of search for information, evaluation of alternatives and choice / purchase. The final model is depicted in Table 7.1 at the end of the next chapter. With regards to the e-servicescape factor visual appeal, the main conclusion was drawn that originality is not a necessity and that it is more important to provide a design that adheres to consumers expectations based on (existing) brand values and that flows fluently from homepage to checkout. Furthermore the importance of product images was stated several times as it can provide context to images and can even transfer emotions and feelings regarding a web shop and specific products. Although not researched often in the past, the role of product photography appears to be one of vital importance to the success of a web shop. More specifically even, discussion focussed towards the effect on conversion rates of using product photography displayed on models. With regards to the e-servicescape factor layout and functionality, two main conclusions were drawn. First of all it showed important to continuously take the end user into account when designing these e- servicescape aspects in a web shop and adhere to expectations of consumers in order to create a logical continuous flow from homepage to checkout. Secondly the role of cross-selling in a web shop was discussed. As in academic literature, contradicting findings were found on the type, location and implementation of cross-selling in a web shop. The discussion revolved around the type of crossselling and the way it should be presented throughout the web shop on the one hand and specifically on the usage of cross-selling on cart page. Both potential benefits, such as an increase in cart value, and potential disadvantages, a decrease in cart-to-purchase conversion due to consumers brought into doubt, were mentioned. With regards to the final e-servicescape factor financial security one of the main conclusion was that it consumers should feel as safe as possible by invoking feelings of trust and security using logos, certificates and statements. At the same time the effect of these cues is limited in case of existing brands and retailers and they can even undermine feelings of trust if they are to prominent and distract the user from entering his personal information and focussing on checking the security of the web shop instead. The second conclusion was that a checkout should be made as easy to complete as possible and that it should provide every payment method that a consumer could potentially want to use, as long as it is well known and don t make other consumers doubt the security of the web shop. Regarding ease of use, the interviewees also focussed on the usage of a single-page or a multi-page 33

42 checkout. In literature little research has been done in this are as of yet, even though getting consumers to complete the checkout process can be seen as a core activity of web shop owners. The validated e-servicescape model shows that different design rules and approaches should be considered for different stages in the consumer decision making process and as such on different pages. In order to generate more academic knowledge on two topics covered by the design rules, two field experiments were performed. Even though conclusions were drawn from these results, notion should be made that the experiments featured a relatively low amount of data points and are as such tentative and specific to the case of the lingerie retailer under discussion. The first experiment focussed on the role of cross-selling on the cart page and its effect on revenue and cart-to-purchase conversion. On the basis of the results it was concluded that elements on the cart page that potentially distract users from proceeding to checkout including, though not exclusively, cross-selling functionality, have a negatively influence on the cart-to-purchase conversion rate. Focus on conversion should however be balanced out against higher cart values and as such revenues which may be increased by means of cross-selling functionality. The second experiment focussed on a recent development in the field: the testing and usage of singlepage and multi-page checkouts. The dataset regarding this experiment was highly spurious, making it impossible to draw definitive conclusions. More research is needed into the topic of checkout design and one should strongly take into account the presence of covariates, of which at least time of day and cart value were identified as important. 34

43 7. Reflection The aim of theoretical research is to contribute to science. However, as with every research there are limitations to the current research that need to be taken into account, which is done in paragraph 7.1. Taken these limitations into account, the academic implications of the research are discussed in paragraph 0, including the identification of future research opportunities. The chapter ends with the identification of managerial implications in paragraph Limitations This thesis research had several limitations to it that are important to consider when establishing generalisations and implications. The limitations can be divided into three categories: limitations due to the research design, limitations due to the research execution and technical limitations Research design The research design, using a combination of academic literature, field based interviews and two field experiments mainly provided limitations on the aspect of generalizability. Although an external case study was part of the original research design, contact with twenty e-commerce companies did not result in the opportunity of interviews. Reasons varied from a lack of interest in cooperating in the research, to insufficient resources partly due to the summer period in which the research was executed, to declining cooperation due to the competitive position of the respondents to the company where the thesis internship was performed. The remaining in-company interviews limit the generalizability of the e-servicescape model as it only focusses on the knowledge of employees in one company, albeit that the interviewees come from different departments of a company that has operated different types of web shops both in the past and at this point in time. As a result the model and design principles established in this case study should be tested, confirmed and deepened out further both at other companies and in different industries than the online apparel and fashion retail industry. Although web shops with fast moving consumer goods, being printer supplies, were covered, the interviewees showed that different design rules may apply based on the web shop owners goal of a web shop: purchase and retention or solely purchase. Next to the theoretical and field work in order to establish the e-servicescape model, two experiments were executed. Three main limitations were present in the experiment design, of which the first was focussed on the limited time span of the experiment. This resulted in the difficult interpretation of the data due to the inability to correctly measure time-based covariates. More data was needed to gain more insights into these covariates, which should also provide the opportunity to investigate interaction effects between variables and the opportunity to design models that perform better on an overall scale. Next to the limitations due to a limited time span, generalizability is threatened by only including a single web shop. In order to be able to better generalize the results, the experiments will need to be repeated on different web shops both in similar and different industries, in order to establish the effect of different design rule interpretation on consumer purchase intentions on a web shop. Moreover, specific attributes, characteristics and propositions of web shops may result in very different results amongst web shops that at first sight appear to be equal. The experiment results do however provide guidelines on the direction of effects and aspects to consider when designing and optimising different web shop pages and clusters of pages. 35

44 Perhaps even the most important limitation regarding the experiment design is however that due to the field nature of the experiments, it was not possible to measure trust and purchase intentions and that instead conversion was chosen as a measurement. This threatened construct validity and internal validity and provides a strong recommendation for future research Research execution The academic literature review and validating interviews provided two main limitations. The first limitation is that, given the scope of the master thesis research, the academic literature needed to balance between being high-level and generic and being detailed. A choice was made to assume the model of Harris and Goode (2010) in the interpretation that three specific e-servicescape factors and sub-factors influence trust and subsequently purchase intentions, and to focus this research on identifying design rules that influence these (sub-)factors. As such the validated e-servicescape model should be tested in a laboratory setting to identify the causal relationships of the design rules and measure their influence on both trust and purchase intentions directly. A second limitation is in the interview execution. The interviews focussed on identifying aspects influencing e-servicescape sub-factors and the testing of design-rules without literally depicting the design rules but by incorporating them into the style of questioning. This was done in order not to direct the interviewees and to gain as much data as was possible. However, this also implies a small limitation to the validity of the e-servicescape model validation. Given the fact that the design rules were however incorporated into the questioning, this was not considered an issue The execution of the experiments resulted in three additional limitations. The first limitation focusses on the translation of the hypotheses into experiments. Given the fact that the experiments were executed at an actual web shop, being operated by a third party company, not all proposed and desired experiment designs could be tested. The necessary approval of both the web shop owner and operator resulted in more conservative experiment designs. This limited the potential effects of experiment variations as they showed more resemblance to one another and as such made it more difficult to derive statistically significant results and conclusions. This was enhanced by the second limitation, also discussed during the discussion of the experiment results, which was the restricted period of time the experiments were allowed to run. This lead to small amounts of data points available that hampered the results analyses and drawing of conclusions: models used were of low performance and interpretation was challenging given the expected role of covariates for which too little data was available on the one hand and given the small differences between the different experiment variations on the other hand. Although both limitations results in limited construct validity and external validity of the research, the results and conclusion do provide directions towards the effects that can be expected when making e-servicescape design decisions as well as the direction of the results of comparable experiments. The time period in which the experiments were conducted created the third limitation; in case of the cart page experiment a sale period occurred and a new collection of swim wear was made available. During the checkout experiment a sale occurred as well. In order to enhance validity, similar experiments should be executed once more during periods with new collection, during sale periods and during periods where there is no strong marketing campaign active. It is expected that the different marketing campaigns attract different types of consumers, for example oriented towards bargains during sale periods, which might result in a preference for displaying a specific type of cart page or checkout during that period. The current results as such provide a direction for expected effects and future research. 36

45 Technical limitations The tools available at the time of the experiment as well as the implementation of the tools provided several limitations to the experiment results It is tenable that the observed results are influenced largely by the (absence of a) marketing campaign active during the experiment period. Therefore it is important to replicate the results of this experiment at other web shops during comparable time frames in order to establish where the balance lies between the design rules focussing on providing navigational functionality and inspirational design on the one hand and guiding users to products of their interest as direct and with as few clicks as possible on the other hand. With respect to the cart page and checkout experiment there were some technical limitations in measuring the amount of information entered by consumers in the case of the checkout experiment and with measuring the cart value in case of both experiments. The inability to measure the amount of data entered in the case of the single-page experiment variation limited the options of building a more refined model next to the high level checkout-to-purchase conversion model, in order to analyse the effect of the checkout variation on entering checkout information and completing a purchase. The cart value was measured at the final page a web shop visitor visited, instead of when the consumers entered the checkout. This implied that inaccurate data might exist where consumers entered the checkout with a cart filled with products, exited the checkout, changed the cart contents and subsequently left the web shop. It was however expected these cases were at a minimum as consumers that exited checkout and cleared out their entire cart were excluded from the dataset based on their cart value of zero. Furthermore the likelihood of this limitation having a major impact on the experiments results was found relatively small and as such does not provide further implications for generalisation and applicability of the conclusion Theoretical contributions and future research opportunities The contributions of this research are threefold. First of all the theoretical e-servicescape model provided an overview of knowledge available in the academic research field on optimising conversion using an e-servicescape perspective. The model provided can both be used to identify research fields that yet require more theoretical investigation and as a starting point for quantifying the effects of certain e-servicescape characteristics, for which this study was too limited. The second contribution of this research lies in the subsequent step of including field data. The field of e-commerce is changing rapidly, leading to past research results that are no longer fully accurate or at worst even obsolete. Although there were limitations to the results of the case study, it did provide insights into the current sentiment and knowledge available in the field regarding the implementation of e-servicescape design rules. This leads towards the identification of future research opportunities and directions both in confirming the results and performing additional explanatory research to further identify the effects of the design rules. The third theoretical contribution of this research was formed by the experiments. They identified the potential strong impact of marketing campaigns on conversion rates at different web shop stages, which requires further explanatory research in case of both experiments. Furthermore, although the scale was of the experiment was too small to provide results, a first step was made in research regarding single-page and multi-page checkouts which provides a first step towards further investigation in the research field. Future research should focus on extending the experiment at different web shops in different industries during different types of marketing campaigns over longer periods of time, in order to provide more insights into factors and moderators playing a role in checkout conversion rates. At the same time future research should focus on further identifying the 37

46 usage of cross-selling in the checkout. Additional dependent variables, types of cross-selling and design choices are needed to gain more insights on the influence of both cross-selling and moderators on conversion rates. As stated, the most important future research opportunities lay in the identification of factors influencing the success of cross-selling in the cart and the use of multi-page and single-page checkouts based on the experiments. In general this research should focus on acquiring additional data to ensure reliability and on quantifying effects and confirming effects identified in this study, and the identification of moderating factors such as time of day, day of week and different marketing campaigns. On the basis of the validated e-servicescape model future research opportunities were also identified as being the identification of the role of using product on model photography over sole product photography and the identification of the role of the category page (whether it should be functional and oriented on navigation, or whether it should be focussed on inspiring visitors). Additionally purchasing online by consumers via mobile communication devices such as smartphones and tablets is becoming more mainstream, which also leads to a need of more research towards the experiment results and on the implementation of design rules in a so-called mobile environment. 38

47 7.3. Managerial implications Next to theoretical contributions, the research also provided several managerial implications. First of all the e-servicescape model combining theory and practice may be used by managers as a tool and guideline on the tactical level when designing or optimising the e-servicescape of a web shop. Although large web shop operators may find the model beneficial, web shops with limited resources or relatively small amounts of visitors that are limited testing abilities could find the model to be a starting point to optimise their web shop on the basis of theoretically and practical grounded knowledge regarding web shop aspects. The final model is depicted in Table 7.1 at the next pages. Additional managerial implications were created by the experiment results regarding the cart page design. Even though intuition and the analogy to offline checkout bargains might lead to the inclusion of cross-selling on the cart page, the functionality may prove detrimental to conversion rate. Careful considerations regarding the implementation of the functionality should be taken into account, as well as multiple dependent variables such as conversion rate and average cart value in order to establish which version works best for a specific web shop. Finally next to the specific implications from the experiments, the importance of both extensive testing and deliberate experiment designs was shown. On the one hand, results might very well not be as expected, but more important the experiment implications should be established meticulously. The experiments part of this research are a clear example of the latter. Small amounts of data lead to difficult analyses and interpretations of data and results which might, in case of wrong types of analyses, result in spurious conclusions based on insufficient or inadequate data or might at least lead to limited generalizability of conclusions even within a single web shop. 39

48 Table 7.1 Validated e-servicescape model Decision making process stage Choice / purchase Evaluation of alternatives Information search Design rule Include original designs and signs such as logos. Animate logos for increased effectiveness and impact, but sparingly to avoid distraction. Whilst adhering to standard and common design rules, make sure the design fits the web shop and brand proposition. If possible, implement references to an offline brand and retailer. Design should be colourful by a good selection, placement and combination of colours. Design should be diverse, by visual richness, dynamics, novelty and creativity. Design should be simple by showing unity, homogeneity, clarity, orderliness and balance. Design should show craftsmanship by modernity and integrating simplicity, diversity and colourfulness. Provide large size high quality product images supported by schematic product characteristics. Provide logos, certificates and other visual cues early on to enhance feelings of trust. Originality of design Visual appeal Do not distract users with aesthetic designs during checkout. The careful use of people on pictures can provide context and transfer emotion and feeling. Create a design that flows fluently from home page to checkout with focus on support for decision and transaction processes Use a consistent tone of voice that suits the target audience. Be scarce with vivid entertainment as it decreases shopping cart use. Create entertainment by providing thoughtful use of colour and typography based on functionality. Create entertainment by social aspects, interactive elements and inspirational design. Provide entertainment by regularly updating the web shop so consumers get the feeling it evolves. Aesthetic appeal Entertainment value 40

49 Table 7.1 Validated e-servicescape model (continued) Decision making process stage Choice / purchase Evaluation of alternatives Information search Usability Relevance of information Customisation Interactivity Design rule Build multiple ways of navigation based on ease-of-use by different types of consumers and the actions it facilitates that continuously shows the breadth and depth of the web shop. Consider that the size and location of text and graphics determine users attention based on F-shaped scanning patterns. Create a clean and uncluttered design, without unnecessary text and graphics and minimum loading times and system crashes, that behaves as user expect. Provide clear organisation and layout without distractions. Provide a link back to shopping. Create a consistent and logical user flow from home page to checkout. Provide contact information, preferably including a (free) number, to reach the consumer support department. State competitive advantages regarding the quality of product offerings and services clearly throughout the web shop. State information regarding price, features, inventory information and order related charges as early on as possible. Provide information that is accurate, consistent and specific, supported by full size pictures. Provide information that is accurate, consistent and specific. Display out-of-stock sizes, but remove permanent out-of-stock products and colours. Provide information from a consumer point of view whilst keeping them in a continuous flow. The location, type and implementation of cross-selling, especially in case of limited data and business rule, should be considered due to conflicting results. Specify customisation towards decision and transaction processes. Add features supporting direct interactivity between visitors and sales or support employees. Add interactive functionality that is potentially useful or influences site usage and navigation. Change text and colours when hovering over actionable text and images. Layout & functionality 41

50 Table 7.1 Decision making process stage Validated e-servicescape model (continued) Choice / purchase Evaluation of alternatives Information search Perceived security Ease of use Design rule Display trusted and independent seals and certificates of approval throughout the web shop. Ask only strictly necessary information and exclude marketing questions. Explicitly state what information is stored and not stored. Display trusted and independent seals and certificates of approval. Create a consistent and logical user flow from home page to checkout. Allow for checkout completion without registration or using an account..provide actionable feedback and error messages and only if strictly necessary Provide information regarding the different checkout steps as well as the current location. Provide the option of credit card payments, regular payment types and payment types that function as the extension of existing methods. Take the products sold and different target audiences into account when designing single or multi-page checkouts both for speed and confirmation. Financial security 42

59 Appendix C. Case study interview questions The interview questions were held in Dutch. An English translation is printed in italic. C.1 Welkom / Welcome o Het interview is vertrouwelijk, dus er kan vrij (zowel lovend als kritisch) gesproken worden. The interview is confidential, so you can speak freely in both positive and negative statements. o Het interview wordt opgenomen (audio) om later uit te kunnen werken en in geval van twijfel over de uitwerking terug te kunnen luisteren. The interview will be recorded (audio) in order to write a transcript and listen to the interview again in case of the need for clarification. o De opnames worden na afloop van het onderzoek vernietigd. The recordings will be destroyed at the end of the thesis research. o o o De uitwerking / inzichten worden ter goedkeuring voorgelegd aan de geïnterviewde. The findings taken from the interview will be shown to the interviewee with a request for approval. Heb je vooraf vragen of opmerkingen? Are there any questions before we begin? Ben je akkoord met bovenstaande punten? Do you acknowledge the points mentioned above? C.2 Inleiding / Introduction o De achtergrond van het onderzoek wordt uitegelegd: Afstudeeronderzoek aan de TU Eindhoven naar conversie optimalisatie. The background of the research is discussed: TU Eindhoven thesis research on conversion optimisation. o Doel van het interview is het verzamelen van best practices en validatie van het model. Aim of the interview is to gather best practices and validate the e-servicescape model used. o Voorbeelden tijdens het interview graag gericht op grote cases. The examples in the interview should preferably be focused on large cases. C.3 Functie / Function o Wat is je achtergrond (opleiding, werkervaring)? What is your background (education, experience)? o Wat is je rol binnen Docdata Commerce? What is your position at Docdata Commerce? o Wat zijn je verantwoordelijkheden? What are your responsibilities? C.4 Optimalisatieproces / Optimisation process o Hoe wordt op dit moment conversie geoptimaliseerd? What are the current practices of conversion optimisation? Appendices - 51

69 D.2 Aggregated results Table D.2 Validated e-servicescape model Ratio 7 / 7 3 / / 6 6 / 6 6 / 6 5 / 5 8 / 8 5 / 5 8 / / 5 6 / 6 7 / 7 1 Case study Expertise Design Marketing Marketing Design Marketing Design Marketing Payments Design Payments Design Marketing Design Decision Decision making process stage Support Support Add Add Support Support Support Support Support Support Support Add Add Add Support Support Support Add Design rule Include original designs and signs such as logos. Animate logos for increased effectiveness and impact, but sparingly to avoid distraction. Whilst adhering to standard and common design rules, make sure the design fits the web shop and brand proposition. If possible, implement references to an offline brand and retailer. Design should be colourful by a good selection, placement and combination of colours. Design should be diverse, by visual richness, dynamics, novelty and creativity. Design should be simple by showing unity, homogeneity, clarity, orderliness and balance. Design should show craftsmanship by modernity and integrating simplicity, diversity and colourfulness. Provide large size high quality product images supported by schematic product characteristics. Provide logos, certificates and other visual cues early on to enhance feelings of trust. Do not distract users with aesthetic designs during checkout. The careful use of people on pictures can provide context and transfer emotion and feeling. Create a design that flows fluently from home page to checkout with focus on support for decision and transaction processes Use a consistent tone of voice that suits the target audience. Be scarce with vivid entertainment as it decreases shopping cart use. Create entertainment by providing thoughtful use of colour and typography based on functionality. Create entertainment by social aspects, interactive elements and inspirational design. Provide entertainment by regularly updating the web shop so consumers get the feeling it evolves. Choice / Purchase Evaluation of alternatives Information search Design rule Originality of design New New Visual appeal New New New Entertainment value New Aesthetic appeal Appendices - 61

70 Table D.2 Validated e-servicescape model (continued) Ratio 6 / 6 8 / 8 6 / 7 8 / 8 4 / / 9 8 / 9 9 / 10 7 / 8 7 / 8 2 / / 8 8 / 8 1 / 3 4 / 6 4 / 4 Case study Expertise Design Marketing Marketing Support Design Marketing Marketing Mgt. director Comments all interviewees Design Marketing Design Marketing Decision Support Decision making process stage Support Support Support Support Add Revise Support Support Support Support Revise Add Revise Support Support Support Support Design rule Build multiple ways of navigation based on ease-of-use by different types of consumers and the actions it facilitates that continuously shows the breadth and depth of the web shop. Consider that the size and location of text and graphics determine users attention based on F-shaped scanning patterns. Create a clean and uncluttered design, without unnecessary text and graphics and minimum loading times and system crashes, that behaves as user expect. Provide clear organisation and layout without distractions. Provide a link back to shopping. Create a consistent and logical user flow from home page to checkout. Provide contact information, preferably including a (free) number, to reach the consumer support department. State competitive advantages regarding the quality of product offerings and services clearly throughout the web shop. State information regarding price, features, inventory information and order related charges as early on as possible. Provide information that is accurate, consistent and specific, supported by full size pictures. Provide information that is accurate, consistent and specific. Display out-of-stock products and sizes, but remove permanent out-of-stock products and colours. Provide information from a consumer point of view whilst keeping them in a continuous flow. Consider where to place customisation as there are conflicting results. The location, type and implementation of cross-selling, especially in case of limited data and business rule, should be considered due to conflicting results. Specify customisation towards decision and transaction processes. Add features supporting direct interactivity between visitors and sales or support employees. Add interactive functionality that is potentially useful or influences site usage and navigation. Change text and colours when hovering over actionable text and images. Choice / Purchase Evaluation of alternatives Information search Design rule Usability New Relevance of information New Customisation Interactivity Layout & functionality Appendices - 62

71 Table D.2 Validated e-servicescape model (continued) Ratio 9 / 11 1 / 2 2 / 2 8 / / 1 3 / 3 9 / 9 8 / 12 7 Case study Expertise Design Marketing Marketing Payments Support Decision Support Decision making process stage Support Support Support Add No support, keep based on literature Support Support Revise Add Design rule Display trusted and independent seals and certificates of approval throughout the web shop. Ask only strictly necessary information and exclude marketing questions. Explicitly state what information is stored and not stored. Display trusted and independent seals and certificates of approval. Create a consistent and logical user flow from home page to checkout. Allow for checkout completion without registration or using an account. Provide actionable feedback and error messages and only if strictly necessary. Provide information regarding the different checkout steps as well as the current location. Provide the option of credit card payments, regular payment types and payment types that function as the extension of existing methods. Take the products sold and different target audiences into account when designing single or multi-page checkouts both for speed and confirmation. Choice / Purchase Evaluation of alternatives Information search Design rule Perceived security New Ease of use New Financial security Appendices - 63

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